When your enterprise data makes off-the-shelf LLMs useless
📰 Medium · Machine Learning
Off-the-shelf LLMs may not work for enterprise data, learn why and how to address this issue
Action Steps
- Identify your enterprise data's unique characteristics
- Assess the limitations of off-the-shelf LLMs for your use case
- Fine-tune an LLM using your enterprise data
- Evaluate the performance of the fine-tuned LLM
- Compare the results with off-the-shelf LLMs
Who Needs to Know This
CTOs, heads of product, and data scientists in regulated industries can benefit from understanding the limitations of off-the-shelf LLMs and how to fine-tune them for their specific use cases
Key Insight
💡 Off-the-shelf LLMs may not perform well on enterprise data due to its unique characteristics, fine-tuning can improve performance
Share This
Off-the-shelf LLMs may not cut it for enterprise data. Fine-tuning can help!
Key Takeaways
Off-the-shelf LLMs may not work for enterprise data, learn why and how to address this issue
Full Article
There’s a pattern I’ve seen play out in regulated industries more times than I can count. A CTO or head of product watches a ChatGPT demo… Continue reading on Medium »
DeepCamp AI